The ongoing goal of our project, "Discovering and applying knowledge in clinical databases," is to develop and apply methods to exploit electronic medical record data for decision support, with an emphasis on narrative data. Since the inception of our project as an R29 in 1994, we have been developing methods for preparing raw electronic medical record data, applying and evaluating natural language processing, developing data mining techniques including machine learning, and putting the results to use for clinical care and research. In this competing continuation, we propose to address the temporal information in the electronic medical record and to apply natural language processing and temporal processing to the task of syndromic surveillance in collaboration with the New York City Department of Health and Mental Hygiene (NYC DOHMH). We have begun work on a temporal processing system. It extracts temporal assertions stated in narrative reports, uses the MedLEE natural language processor to parse the non-temporal information, infers implicit temporal assertions based on a knowledge base, and produces the information in the form of a simple temporal constraint satisfaction problem. The latter can be used to answer questions about the time of events and the temporal relation between pairs of events. We propose to complete the system, expand the knowledge base, speed computation, address the uncertainty of temporal assertions, incorporate temporal information from structured data, and evaluate the system. NYC DOHMH has a mature syndromic surveillance system that watches over almost eight million persons, and it has as-yet unexploited data sources in the form of narrative and structured electronic medical records. We propose to apply natural language processing and our proposed temporal processing to convert the data to a form appropriate for surveillance. We will evaluate the incremental benefit of structured data, narrative data, and temporally processed narrative data.